Biological and genomic characteristics of three novel bacteriophages and a phage-plasmid of <i>Klebsiella pneumoniae</i>
Bibliographic record
Abstract
Bacteriophages have emerged as promising candidates for the treatment of difficult-to-treat bacterial infections. The aim of this study is to isolate and characterize phages infecting carbapenem-resistant and extended-spectrum beta-lactamase producer Klebsiella pneumoniae isolates. Water samples were taken for the isolation of bacteriophages. One-step growth curve, the optimal multiplicity of infection (MOI), thermal and pH stabilities, transmission electron microscopy and whole-genome sequencing of phages were studied. Four phages were isolated and named Klebsiella phage Kpn02, Kpn17, Kpn74, and Kpn13. The optimal MOI and latent periods of phage Kpn02, Kpn17, Kpn74, and Kpn13 were 10, 1, 0.001, and 100 PFU/CFU and 20, 10, 20, and 30 min, respectively. Burst sizes ranged from 811 to 2363. No known antibiotic resistance and virulence genes were identified. No tRNAs were detected except Klebsiella phage Kpn02 which encodes 24 tRNAs. Interestingly, Klebsiella phage Kpn74 was predicted to be a lysogenic phage whose prophage is a linear plasmid molecule with covalently closed ends. Of the Klebsiella-infecting phages presented in current study, virulent phages suggest that they may represent candidate therapeutic agents against MDR K. pneumoniae, based on short latent period, high burst sizes and no known antibiotic resistance and virulence genes in their genomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".